Investor sentiment and herding behavior in financial markets: a bibliometric analysis

Purpose This study aims to conduct a bibliometric analysis to provide a comprehensive overview of the existing literature on investor sentiment and herding behavior in financial markets and to identify promising avenues for future research. Design/methodology/approach A total of 2,351 articles published between 1976 and 2024 were retrieved from the Scopus database. The analysis was carried out using the Bibliometrix R package and VOSviewer. A performance analysis was conducted to examine publication trends, leading authors, key journals and the most influential studies. Additionally, a science mapping approach, specifically bibliographic coupling and thematic evolution, was used to explore the intellectual structure of the field. Findings The findings indicate a significant growth in scholarly output over the past four decades, reflecting increasing academic interest in the behavioral aspects of financial decision-making. Bibliometric mapping identifies four major thematic clusters: drivers and global financial implications of investor sentiment, investor sentiment as a tool for market forecasting and volatility analysis, behavioral anomalies and collective behavior in financial decision-making and patterns and determinants of herding across global financial markets. The analysis also highlights the evolving nature of the field, with recent research increasingly integrating sentiment analytics and advanced computational techniques to understand investor behavior and financial market dynamics. Originality/value This study takes a pioneering approach by integrating bibliometric analysis of both investor sentiment and herding behavior within the context of financial markets. It maps the conceptual and intellectual structure of the field, highlights key thematic areas and offers future research directions, making it a valuable contribution to the advancement of the behavioral finance field.